It is well known that people dislike queuing. Extensive research has shown that queuing at an electronic point of service such as a checkout in a retail outlet is one of the main causes of customer dissatisfaction with the service offered. Conversely, short queues and, perhaps more critically, the expectation of short queues, are key ways to build customer loyalty and satisfaction.
The retail outlet experience is repeated across other business sectors. From banks to theme parks, reducing queuing and making more effective use of service point staff and customer service representatives is a business imperative.
Whilst some existing queue learning algorithms are adapted to learn a queue path through a service point area, a major problem encountered is of discriminating between customers waiting to be served and those already in transaction. U.S. Pat. No. 7,702,132 discloses a system which determines queue lengths by associating a first customer to a ‘seed’ location located at an electronic point of service and iteratively joining customers who exhibit queue-like behavior to the back of the queue thus formed. This system creates a separate queue for each electronic point of service and is thus not readily applicable to situations where multiple service locations may be fed from a single queue line.